397 citations · 951 across the 17 of their papers we have counts for
5 papers · 1 filter
Forecasting Transportation Network Speed Using Deep Capsule Networks with Nested LSTM Models
Xiaolei Ma, Yi Li, Zhiyong Cui +1
Accurate and reliable traffic forecasting for complicated transportation networks is of vital importance to modern transportation management. The complicated spatial dependencies o…
Multistep Speed Prediction on Traffic Networks: A Graph Convolutional Sequence-to-Sequence Learning Approach with Attention Mechanism
Zhengchao Zhang, Meng Li, Xi Lin +2
Multistep traffic forecasting on road networks is a crucial task in successful intelligent transportation system applications. To capture the complex non-stationary temporal dynami…
Traffic Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting
Zhiyong Cui, Kristian Henrickson, Ruimin Ke +2
Traffic forecasting is a particularly challenging application of spatiotemporal forecasting, due to the time-varying traffic patterns and the complicated spatial dependencies on ro…
A Deep Generative Adversarial Architecture for Network-Wide Spatial-Temporal Traffic State Estimation
Yunyi Liang, Zhiyong Cui, Yu Tian +2
This study proposes a deep generative adversarial architecture (GAA) for network-wide spatial-temporal traffic state estimation. The GAA is able to combine traffic flow theory with…
Deep Bidirectional and Unidirectional LSTM Recurrent Neural Network for Network-wide Traffic Speed Prediction
Zhiyong Cui, Ruimin Ke, Ziyuan Pu +1
Short-term traffic forecasting based on deep learning methods, especially long short-term memory (LSTM) neural networks, has received much attention in recent years. However, the p…